Background and Clinical Significance: Concomitant severe aortic stenosis (AS) and abdominal aortic aneurysm (AAA) in elderly patients presents a significant therapeutic challenge. While transcatheter aortic valve replacement (TAVR) and endovascular aneurysm repair (EVAR) have become established minimally invasive treatments for high-risk patients, simultaneous management of both conditions remains rare. Case Presentation: We report the first documented case in Serbia of a simultaneous TAVR and EVAR in a 75-year-old male with severe symptomatic AS and AAA. The patient had a history of hypertension, diabetes mellitus, atrial fibrillation, prior radiofrequency pulmonary vein ablation, and pacemaker implantation. Echocardiography demonstrated severe AS with a transvalvular gradient of 116/61 mmHg, an aortic valve area of 0.6 cm2, and a left ventricular ejection fraction of 30–35%. Coronary angiography revealed 50–60% stenosis of the right coronary artery. Following evaluation by a multidisciplinary Heart and Vascular Team, a combined procedure was performed under general anesthesia via bilateral femoral access. TAVR with a Medtronic Evolut R valve was successfully deployed, followed by EVAR with satisfactory stent graft positioning and angiographic results. The patient’s postoperative course was uneventful, and he was discharged on the ninth day. At six-month follow-up, echocardiography showed optimal valve function, and CT identified a type II endoleak, which was managed conservatively. Conclusions: This case demonstrates the feasibility and safety of simultaneous TAVR and EVAR in a high-risk elderly patient, emphasizing the importance of careful preoperative planning and a coordinated multidisciplinary approach. Further studies are warranted to establish standardized guidelines for the management of patients with coexisting severe AS and AAA.

The cement industry is under constant pressure to reduce its environmental footprint while ensuring economic competitiveness and technological reliability. One of the most effective strategies to achieve this goal is the substitution of traditional raw materials with alternative ones derived from industrial (by)products, waste, or secondary resources. This paper presents a structured methodology for the selection and evaluation of potential raw materials for clinker production. The proposed approach integrates four key criteria: physical compatibility, which determines whether the raw material can be handled by existing processing equipment; chemical compatibility, which ensures compliance with clinker quality requirements; environmental compliance, which assesses adherence to local and international environmental regulations; and economic viability, including the costs of material acquisition, processing, equipment adaptation, and CO2 emissions associated with the raw mix. The research procedure involves initial communication with suppliers, visual inspection of the material, laboratory analysis (chemical and environmental), raw mix modelling, and full economic evaluation. If at any stage the material fails to meet the required criteria, feedback is provided to the supplier, avoiding unnecessary costs and efforts. Results indicate that this integrated methodology offers a systematic and transparent making of decision framework that can accelerate the acceptance of alternative raw materials, improve resource efficiency, and contribute to sustainable cement production.

Understanding meat categorization is a fundamental component of veterinary education, especially within the context of food hygiene and public health. Veterinary students must grasp legal classifications of meat, which depend on variables such as species, age, quality, and processing techniques. This knowledge is essential for accurate meat inspection, labeling, and compliance with both national and international food safety standards. Despite prior exposure to muscle anatomy in anatomy course, students often face challenges in applying this knowledge to practical meat classification tasks. This study aimed to assess the effectiveness of three distinct instructional methods in improving veterinary students’ ability to identify meat categories and associated muscle structures: traditional classroom teaching, computer-based instruction using 3D models, and immersive virtual reality (VR). Participants included fourth-year veterinary students during the summer semester of the 2024/2025 academic year. To facilitate digital learning, a dedicated 3D model library “3DMeat” was developed as well as virtual reality environment. Results indicate that technology-enhanced instructional approaches, can significantly enhance student engagement and understanding of complex topics such as meat categorization. Initial test scores were highest in the group using 3D models (16.3 ± 4.1), followed by the traditional lecture-based group (15.6 ± 3.07), and the VR group (11.7 ± 5.1). However, a follow-up assessment conducted 2 weeks later revealed that VR group demonstrated the highest retention of knowledge. These findings suggest that although immediate performance may vary, immersive learning environments such as VR can foster stronger medium-term retention of complex material.

Background: Breast cancer remains the most common cancer in women worldwide. Treatment has evolved into multimodal approaches, with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) serving as a key prognostic marker. The aim of this study was to evaluate the value of inflammatory markers in predicting pCR to NAC in breast cancer. Methods: This cross-sectional study of 74 patients with breast cancer who underwent NAC followed by surgery included demographic, tumor, and immune-inflammatory marker data. Receiver operating characteristic curve analysis and the Youden index were used to determine optimal cutoff values. Univariate and multivariate logistic regression assessed associations between markers and pCR, adjusting for tumor stage, human epidermal growth factor receptor 2 (HER2), and estrogen receptor (ER) status. Results: Our multivariate analysis identified the pan-immune-inflammation value (PIV), HER2 status, and ER status as significant independent predictors of pCR. PIV (OR, 4.28; 95% CI, 1.59–16.88) remained significant among inflammatory markers, while the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) did not. HER2-positive (OR, 7.45; 95% CI, 2.30–24.15) and hormone receptor (HR)–negative (OR, 7.02; 95% CI, 2.63–18.70) statuses were also strongly associated with pCR. Conclusion: PIV is a robust predictor of pCR in patients with breast cancer receiving NAC, offering a comprehensive reflection of the immune-inflammatory state. Incorporating PIV with tumor-specific markers (e.g., receptor status, Ki-67, grade) may enhance treatment stratification. Further validation in diverse cohorts is warranted.

Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, they often overlook the critical role of regularization in ensuring robustness and anatomical plausibility. We propose DARE (Deformable Adaptive Regularization Estimator), a novel registration framework that dynamically adjusts elastic regularization based on the gradient norm of the deformation field. Our approach integrates strain and shear energy terms, which are adaptively modulated to balance stability and flexibility. To ensure physically realistic transformations, DARE includes a folding-prevention mechanism that penalizes regions with negative deformation Jacobian. This strategy mitigates non-physical artifacts such as folding, avoids over-smoothing, and improves both registration accuracy and anatomical plausibility

This study examines job performance among judo referees through the lens of personality traits during World Judo Tour events from 2018 to 2022. Sixty-three referees completed an online questionnaire including the Big Five Inventory (BFI) and the Conditions for Work Effectiveness Questionnaire (CWEQ-II). Data were analyzed using descriptive statistics, correlation analysis, and structural equation modeling (SEM). The measurement model showed acceptable validity and reliability, confirming the structural model. Support and resources emerged as the most influential factors affecting job satisfaction (JAS) and organizational role satisfaction (ORS). Incorporating refereeing experience at major events into the model indicated only partial model fit. Findings highlight the role of structural empowerment in mitigating job dissatisfaction among referees. Future research with larger samples should further strengthen the understanding of the relationship between personality traits, empowerment, and job performance.

ObjectiveTo evaluate the predictive value of LA strain parameters and LASI for AF recurrence following electrical CV, and to compare them to conventional echocardiographic, biochemical, and clinical markers.MethodsIn this prospective, observational pilot study, 31 patients with persistent AF underwent electrical CV and were followed for six months. Echocardiographic evaluation included LA reservoir, conduit, and contractile strain, left atrial stiffness index, left atrial volume index (LAVI), left atrial appendage (LAA) morphology, left ventricular ejection fraction (LVEF), right atrial (RA) area, and right ventricular systolic pressure (RVSP). AF recurrence was assessed at three and six months.ResultsAt three months post-CV, LA reservoir, conduit, and contractile strain values were significantly negatively associated with AF recurrence (p < 0.001), while LASI and E/E' ratios were positively associated (p < 0.001). At six months, only contractile strain retained prognostic significance (p = 0.008). LVEF showed a positive correlation with recurrence at six months (p = 0.003), potentially reflecting the role of diastolic dysfunction.ConclusionLA strain parameters and LASI are valuable tools for predicting AF recurrence after CV, particularly in the early post-procedural period. Contractile strain may serve as a more reliable long-term predictor, emphasizing the importance of longitudinal atrial function assessment in rhythm outcome prediction. However, given the small sample size and single-center design, these results should be considered hypothesis-generating, requiring validation in larger studies.

Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge.

Hepatitis E virus (HEV) is foodborne zoonotic pathogen widespread among European swine yet unstudied in Bosnia and Herzegovina (B&H). We estimated HEV seroprevalence in domestic pigs in Federation of B&H (FB&H) and assessed farm-level risk factors for exposure.Cross-sectional survey sampled 437 pigs from 87 farms across seven cantons via two-stage random design. Serum anti-HEV IgG measured by commercial indirect ELISA; managers completed standardized biosecurity/management questionnaire. Apparent seroprevalence calculated with 95% CIs. Univariable screening (α = 0.10) informed multivariable logistic regression with farm-level clustering; collinearity checked (Phi), AIC-guided forward selection applied.Animal-level seroprevalence 77.1% (95% CI 73.0–81.0%); herd-level 95.4% (88.9–98.7%). Adults showed higher seropositivity than growers (91.0% vs. 71.7%; p < 0.001). Significant factors: wild-boar proximity (adjusted POR 3.11; p = 0.04), small farm size (18.35; p < 0.001), swill feeding (5.70; p = 0.03). Cleaning ≥5×/month strongly protective (0.01; p < 0.001). All surveyed cantons had positives; no equivocal ELISA results.Findings indicate widespread HEV in FB&H swine with environmental, food-safety, and occupational implications. Older-animal pattern reflects cumulative exposure; small-farm context and wildlife interface likely sustain transmission, whereas frequent cleaning reduces risk. Strengthened biosecurity, wildlife exclusion, feed oversight (including prohibition/monitoring of swill feeding), and improved hygiene, should form basis of One Health interventions to mitigate potential zoonotic transmission via the pork production chain.

Energy security is currently one of the most important topics worldwide. Maintaining a reliable energy supply is one of the biggest challenges in security science. Additionally, defending energy infrastructure from cyberattacks is an ongoing issue. Understanding the vulnerabilities of energy infrastructure, especially the Smart Grid, which relies on information technology and communications, is a significant advantage. Understanding which system vulnerabilities lead to specific cyber threats presents a significant opportunity, enhancing the defence of energy infrastructure. This paper uses a systematic literature review to identify the most common cyber threat and Smart Grid vulnerability mentioned and researched in the literature from 2018 to 2025. This paper also aims to map the vulnerabilities that allow for cyber threats to occur, with the idea that if we know what causes a weak spot, we can effectively prevent it. Identifying specific weaknesses that could lead to cyber threats allows us to mitigate these dangers by addressing and correcting those vulnerabilities.